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Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma

INTRODUCTION: Meningiomas are the most common primary central nervous system tumors. Predicting the grade and proliferative activity of meningiomas would influence therapeutic strategies. We aimed to apply the multiple parameters from preoperative diffusion tensor images for predicting meningioma gr...

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Autores principales: Takahashi, Yuki, Oishi, Naoya, Yamao, Yukihiro, Kunieda, Takeharu, Kikuchi, Takayuki, Fukuyama, Hidenao, Miyamoto, Susumu, Arakawa, Yoshiki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570481/
https://www.ncbi.nlm.nih.gov/pubmed/37644780
http://dx.doi.org/10.1002/brb3.3201
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author Takahashi, Yuki
Oishi, Naoya
Yamao, Yukihiro
Kunieda, Takeharu
Kikuchi, Takayuki
Fukuyama, Hidenao
Miyamoto, Susumu
Arakawa, Yoshiki
author_facet Takahashi, Yuki
Oishi, Naoya
Yamao, Yukihiro
Kunieda, Takeharu
Kikuchi, Takayuki
Fukuyama, Hidenao
Miyamoto, Susumu
Arakawa, Yoshiki
author_sort Takahashi, Yuki
collection PubMed
description INTRODUCTION: Meningiomas are the most common primary central nervous system tumors. Predicting the grade and proliferative activity of meningiomas would influence therapeutic strategies. We aimed to apply the multiple parameters from preoperative diffusion tensor images for predicting meningioma grade and proliferative activity. METHODS: Nineteen patients with low‐grade meningiomas and eight with high‐grade meningiomas were included. For the prediction of proliferative activity, the patients were divided into two groups: Ki‐67 monoclonal antibody labeling index (MIB‐1 LI) < 5% (lower MIB‐1 LI group; n = 18) and MIB‐1 LI ≥ 5% (higher MIB‐1 LI group; n = 9). Six features, diffusion‐weighted imaging, fractional anisotropy, mean, axial, and radial diffusivities, and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. The two‐level clustering approach for a self‐organizing map followed by the K‐means algorithm was applied to cluster a large number of input vectors with the six features. We also validated whether the diffusion tensor‐based clustered image (DTcI) was helpful for predicting preoperative meningioma grade or proliferative activity. RESULTS: The sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic curves from the 16‐class DTcIs for differentiating high‐ and low‐grade meningiomas were 0.870, 0.901, 0.891, and 0.959, and those from the 10‐class DTcIs for differentiating higher and lower MIB‐1 LIs were 0.508, 0.770, 0.683, and 0.694, respectively. The log‐ratio values of class numbers 13, 14, 15, and 16 were significantly higher in high‐grade meningiomas than in low‐grade meningiomas (p < .001). With regard to MIB‐1 LIs, the log‐ratio values of class numbers 8, 9, and 10 were higher in meningiomas with higher MIB‐1 groups (p < .05). CONCLUSION: The multiple diffusion tensor imaging‐based parameters from the voxel‐based DTcIs can help differentiate between low‐ and high‐grade meningiomas and between lower and higher proliferative activities.
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spelling pubmed-105704812023-10-14 Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma Takahashi, Yuki Oishi, Naoya Yamao, Yukihiro Kunieda, Takeharu Kikuchi, Takayuki Fukuyama, Hidenao Miyamoto, Susumu Arakawa, Yoshiki Brain Behav Original Articles INTRODUCTION: Meningiomas are the most common primary central nervous system tumors. Predicting the grade and proliferative activity of meningiomas would influence therapeutic strategies. We aimed to apply the multiple parameters from preoperative diffusion tensor images for predicting meningioma grade and proliferative activity. METHODS: Nineteen patients with low‐grade meningiomas and eight with high‐grade meningiomas were included. For the prediction of proliferative activity, the patients were divided into two groups: Ki‐67 monoclonal antibody labeling index (MIB‐1 LI) < 5% (lower MIB‐1 LI group; n = 18) and MIB‐1 LI ≥ 5% (higher MIB‐1 LI group; n = 9). Six features, diffusion‐weighted imaging, fractional anisotropy, mean, axial, and radial diffusivities, and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. The two‐level clustering approach for a self‐organizing map followed by the K‐means algorithm was applied to cluster a large number of input vectors with the six features. We also validated whether the diffusion tensor‐based clustered image (DTcI) was helpful for predicting preoperative meningioma grade or proliferative activity. RESULTS: The sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic curves from the 16‐class DTcIs for differentiating high‐ and low‐grade meningiomas were 0.870, 0.901, 0.891, and 0.959, and those from the 10‐class DTcIs for differentiating higher and lower MIB‐1 LIs were 0.508, 0.770, 0.683, and 0.694, respectively. The log‐ratio values of class numbers 13, 14, 15, and 16 were significantly higher in high‐grade meningiomas than in low‐grade meningiomas (p < .001). With regard to MIB‐1 LIs, the log‐ratio values of class numbers 8, 9, and 10 were higher in meningiomas with higher MIB‐1 groups (p < .05). CONCLUSION: The multiple diffusion tensor imaging‐based parameters from the voxel‐based DTcIs can help differentiate between low‐ and high‐grade meningiomas and between lower and higher proliferative activities. John Wiley and Sons Inc. 2023-08-29 /pmc/articles/PMC10570481/ /pubmed/37644780 http://dx.doi.org/10.1002/brb3.3201 Text en © 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Takahashi, Yuki
Oishi, Naoya
Yamao, Yukihiro
Kunieda, Takeharu
Kikuchi, Takayuki
Fukuyama, Hidenao
Miyamoto, Susumu
Arakawa, Yoshiki
Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title_full Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title_fullStr Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title_full_unstemmed Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title_short Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
title_sort voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570481/
https://www.ncbi.nlm.nih.gov/pubmed/37644780
http://dx.doi.org/10.1002/brb3.3201
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